About Optics & Photonics TopicsOSA Publishing developed the Optics and Photonics Topics to help organize its diverse content more accurately by topic area. This topic browser contains over 2400 terms and is organized in a three-level hierarchy. Read more.

Topics can be refined further in the search results. The Topic facet will reveal the high-level topics associated with the articles returned in the search results.

Abstract

In ultrasound (US), optical coherence tomography, synthetic aperture radar, and other coherent imaging systems, images are corrupted by multiplicative speckle noise that obscures image interpretation. An anisotropic diffusion (AD) method based on the Gabor transform, named Gabor-based anisotropic diffusion (GAD), is presented to suppress speckle in medical ultrasonography. First, an edge detector using the Gabor transform is proposed to capture directionality of tissue edges and discriminate edges from noise. Then the edge detector is embedded into the partial differential equation of AD to guide the diffusion process and iteratively denoise images. To enhance GAD’s adaptability, parameters controlling diffusion are determined from a fully formed speckle region that is automatically detected. We evaluate the GAD on synthetic US images simulated with three models and clinical images acquired in vivo. Compared with seven existing speckle reduction methods, the GAD is superior to other methods in terms of noise reduction and detail preservation.

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Table 1.

Denoising Performance for Synthetic US Images with Three Simulation Modelsa

Multiplicative Model (noise variance=0.1)

Field II Simulation

Convolution Model

PSNR (dB)

MSSIM

FOM

SDFFSR

CNR

PSNR (dB)

MSSIM

FOM

SDFFSR

CNR

PSNR (dB)

MSSIM

FOM

SDFFSR

CNR

Noisy image

14.85

0.142

0.183

40.52

0.93

18.91

0.307

0.116

17.01

2.69

23.06

0.439

0.096

15.29

1.39

Lee

23.40

0.524

0.184

17.84

2.48

20.26

0.475

0.116

13.81

4.03

23.77

0.595

0.095

11.55

1.89

AD

28.06

0.870

0.222

7.50

5.60

22.33

0.785

0.114

9.11

6.82

29.58

0.898

0.102

5.85

3.78

SRAD

29.02

0.916

0.327

5.54

8.34

21.71

0.716

0.136

10.97

6.57

29.52

0.913

0.211

4.31

4.66

LPND

27.98

0.893

0.231

2.77

11.12

22.62

0.807

0.111

6.44

6.37

30.08

0.911

0.093

4.85

4.57

CD

22.56

0.513

0.184

14.64

2.15

20.85

0.544

0.114

12.85

4.49

25.76

0.684

0.095

9.93

2.23

NLM

26.71

0.859

0.196

3.87

9.73

22.75

0.822

0.105

8.46

6.31

30.11

0.916

0.088

5.20

4.19

JSNLM

27.65

0.833

0.309

4.87

6.14

22.75

0.859

0.155

1.78

9.68

31.37

0.946

0.280

2.37

10.66

GAD

30.82

0.967

0.781

1.83

17.03

22.52

0.871

0.289

0.97

25.72

31.17

0.951

0.591

2.76

8.22

a The best values of quantitative indices are denoted in bold fonts and the second best in italic fonts. FOM, Pratt’s figure of merit; MSSIM, mean structural similarity; PSNR, peak signal-to-noise ratio; SDFFSR, standard deviation of intensities at the fully formed speckle region (FFSR); CNR, contrast-to-noise ratio.

a The best values of quantitative indices are denoted in bold fonts and the second best in italic fonts. SDFFSR, standard deviation of intensities at the fully formed speckle region (FFSR); CNR, contrast-to-noise ratio.

Tables (2)

Table 1.

Denoising Performance for Synthetic US Images with Three Simulation Modelsa

Multiplicative Model (noise variance=0.1)

Field II Simulation

Convolution Model

PSNR (dB)

MSSIM

FOM

SDFFSR

CNR

PSNR (dB)

MSSIM

FOM

SDFFSR

CNR

PSNR (dB)

MSSIM

FOM

SDFFSR

CNR

Noisy image

14.85

0.142

0.183

40.52

0.93

18.91

0.307

0.116

17.01

2.69

23.06

0.439

0.096

15.29

1.39

Lee

23.40

0.524

0.184

17.84

2.48

20.26

0.475

0.116

13.81

4.03

23.77

0.595

0.095

11.55

1.89

AD

28.06

0.870

0.222

7.50

5.60

22.33

0.785

0.114

9.11

6.82

29.58

0.898

0.102

5.85

3.78

SRAD

29.02

0.916

0.327

5.54

8.34

21.71

0.716

0.136

10.97

6.57

29.52

0.913

0.211

4.31

4.66

LPND

27.98

0.893

0.231

2.77

11.12

22.62

0.807

0.111

6.44

6.37

30.08

0.911

0.093

4.85

4.57

CD

22.56

0.513

0.184

14.64

2.15

20.85

0.544

0.114

12.85

4.49

25.76

0.684

0.095

9.93

2.23

NLM

26.71

0.859

0.196

3.87

9.73

22.75

0.822

0.105

8.46

6.31

30.11

0.916

0.088

5.20

4.19

JSNLM

27.65

0.833

0.309

4.87

6.14

22.75

0.859

0.155

1.78

9.68

31.37

0.946

0.280

2.37

10.66

GAD

30.82

0.967

0.781

1.83

17.03

22.52

0.871

0.289

0.97

25.72

31.17

0.951

0.591

2.76

8.22

a The best values of quantitative indices are denoted in bold fonts and the second best in italic fonts. FOM, Pratt’s figure of merit; MSSIM, mean structural similarity; PSNR, peak signal-to-noise ratio; SDFFSR, standard deviation of intensities at the fully formed speckle region (FFSR); CNR, contrast-to-noise ratio.

a The best values of quantitative indices are denoted in bold fonts and the second best in italic fonts. SDFFSR, standard deviation of intensities at the fully formed speckle region (FFSR); CNR, contrast-to-noise ratio.